

import itertools
import string
from collections.abc import Generator, Iterable







def      A_ (       _UpperCAmelCase							,      _UpperCAmelCase       ):
						SCREAMING_SNAKE_CASE_:					List[Any]    						=			iter(_lowerCamelCase       )
						while True:
												SCREAMING_SNAKE_CASE_:					Any    						=			tuple(itertools.islice(_lowerCamelCase							,      _lowerCamelCase       )       )
												if not chunk:
																		return
												yield chunk







def      A_ (       _UpperCAmelCase       ):
						SCREAMING_SNAKE_CASE_:					int    						=			"".join([c.upper() for c in dirty if c in string.ascii_letters]       )
						SCREAMING_SNAKE_CASE_:					Optional[int]    						=			""

						if len(_lowerCamelCase       ) < 2:
												return dirty

						for i in range(len(_lowerCamelCase       ) - 1       ):
												clean += dirty[i]

												if dirty[i] == dirty[i + 1]:
																		clean += "X"

						clean += dirty[-1]

						if len(_lowerCamelCase       ) & 1:
												clean += "X"

						return clean







def      A_ (       _UpperCAmelCase       ):
						SCREAMING_SNAKE_CASE_:					Optional[int]    						=			"ABCDEFGHIKLMNOPQRSTUVWXYZ"
						# we're using a list instead of a '2d' array because it makes the math
						# for setting up the table and doing the actual encoding/decoding simpler
						SCREAMING_SNAKE_CASE_:					List[Any]    						=			[]

						# copy key chars into the table if they are in `alphabet` ignoring duplicates
						for char in key.upper():
												if char not in table and char in alphabet:
																		table.append(_lowerCamelCase       )

    # fill the rest of the table in with the remaining alphabet chars
						for char in alphabet:
												if char not in table:
																		table.append(_lowerCamelCase       )

						return table







def      A_ (       _UpperCAmelCase							,      _UpperCAmelCase       ):
						SCREAMING_SNAKE_CASE_:					List[str]    						=			generate_table(_lowerCamelCase       )
						SCREAMING_SNAKE_CASE_:					Optional[Any]    						=			prepare_input(_lowerCamelCase       )
						SCREAMING_SNAKE_CASE_:					Optional[int]    						=			""

						# https://en.wikipedia.org/wiki/Playfair_cipher#Description
						for chara, chara in chunker(_lowerCamelCase							,      2       ):
												SCREAMING_SNAKE_CASE_:					int    						=			divmod(table.index(_lowerCamelCase       )							,      5       )
												SCREAMING_SNAKE_CASE_:					str    						=			divmod(table.index(_lowerCamelCase       )							,      5       )

												if rowa == rowa:
																		ciphertext += table[rowa * 5 + (cola + 1) % 5]
																		ciphertext += table[rowa * 5 + (cola + 1) % 5]
												elif cola == cola:
																		ciphertext += table[((rowa + 1) % 5) * 5 + cola]
																		ciphertext += table[((rowa + 1) % 5) * 5 + cola]
												else:  # rectangle
																		ciphertext += table[rowa * 5 + cola]
																		ciphertext += table[rowa * 5 + cola]

						return ciphertext







def      A_ (       _UpperCAmelCase							,      _UpperCAmelCase       ):
						SCREAMING_SNAKE_CASE_:					int    						=			generate_table(_lowerCamelCase       )
						SCREAMING_SNAKE_CASE_:					List[str]    						=			""

						# https://en.wikipedia.org/wiki/Playfair_cipher#Description
						for chara, chara in chunker(_lowerCamelCase							,      2       ):
												SCREAMING_SNAKE_CASE_:					Optional[int]    						=			divmod(table.index(_lowerCamelCase       )							,      5       )
												SCREAMING_SNAKE_CASE_:					List[str]    						=			divmod(table.index(_lowerCamelCase       )							,      5       )

												if rowa == rowa:
																		plaintext += table[rowa * 5 + (cola - 1) % 5]
																		plaintext += table[rowa * 5 + (cola - 1) % 5]
												elif cola == cola:
																		plaintext += table[((rowa - 1) % 5) * 5 + cola]
																		plaintext += table[((rowa - 1) % 5) * 5 + cola]
												else:  # rectangle
																		plaintext += table[rowa * 5 + cola]
																		plaintext += table[rowa * 5 + cola]

						return plaintext








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowercase    :   Optional[int]						=			{
    'configuration_mobilevit': ['MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MobileViTConfig', 'MobileViTOnnxConfig'],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase    :   List[str]						=			['MobileViTFeatureExtractor']
		lowercase    :   Tuple						=			['MobileViTImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase    :   List[Any]						=			[
		    'MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'MobileViTForImageClassification',
		    'MobileViTForSemanticSegmentation',
		    'MobileViTModel',
		    'MobileViTPreTrainedModel',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase    :   List[Any]						=			[
		    'TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFMobileViTForImageClassification',
		    'TFMobileViTForSemanticSegmentation',
		    'TFMobileViTModel',
		    'TFMobileViTPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_mobilevit import MobileViTFeatureExtractor
				from .image_processing_mobilevit import MobileViTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mobilevit import (
				    MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MobileViTForImageClassification,
				    MobileViTForSemanticSegmentation,
				    MobileViTModel,
				    MobileViTPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_mobilevit import (
				    TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFMobileViTForImageClassification,
				    TFMobileViTForSemanticSegmentation,
				    TFMobileViTModel,
				    TFMobileViTPreTrainedModel,
				)


else:
		import sys

		lowercase    :   Optional[int]						=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)